{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T22:09:39Z","timestamp":1740175779768,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:00:00Z","timestamp":1715385600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:00:00Z","timestamp":1715385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["202103021224285"],"award-info":[{"award-number":["202103021224285"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1931209"],"award-info":[{"award-number":["U1931209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Robust matching, especially the number, precision and distribution of feature point matching, directly affects the effect of 3D reconstruction. However, the existing methods rarely consider these three aspects comprehensively to improve the quality of feature matching, which in turn affects the effect of 3D reconstruction. Therefore, to effectively improve the quality of 3D reconstruction, we propose a circle-based enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction named EMC+GD_C. Firstly, a circle-based neighborhood division method is proposed, which increases the number of initial matching points. Secondly, to improve the precision of feature point matching, on the one hand, we put forward the idea of enhancing motion consistency, reducing the mismatch of high similarity feature points by enhancing the judgment conditions of true and false matching points; on the other hand, we combine the RANSAC optimization method to filter out the outliers and further improve the precision of feature point matching. Finally, a novel guided diffusion idea combining guided matching and motion consistency is proposed, which expands the distribution range of feature point matching and improves the stability of 3D models. Experiments on 8 sets of 908 pairs of images in the public 3D reconstruction datasets demonstrate that our method can achieve better matching performance and show stronger stability in 3D reconstruction. Specifically, EMC+GD_C achieves an average improvement of 24.07% compared to SIFT-based ratio test, 9.18% to GMS and 1.94% to EMC+GD_G in feature matching precision.<\/jats:p>","DOI":"10.1007\/s40747-024-01461-9","type":"journal-article","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T16:01:36Z","timestamp":1715443296000},"page":"5569-5583","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EMC+GD_C: circle-based enhanced motion consistency and guided diffusion feature matching for 3D reconstruction"],"prefix":"10.1007","volume":"10","author":[{"given":"Zhenjiao","family":"Cai","sequence":"first","affiliation":[]},{"given":"Sulan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jifu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lihua","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jianghui","family":"Cai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,11]]},"reference":[{"issue":"1","key":"1461_CR1","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1007\/s40747-022-00837-z","volume":"9","author":"L Tian","year":"2023","unstructured":"Tian L, Cheng X, Honda M et al (2023) Multi-view 3D human pose reconstruction based on spatial confidence point group for jump analysis in figure skating. Complex Intelli Syst 9(1):865\u2013879","journal-title":"Complex Intelli Syst"},{"key":"1461_CR2","doi-asserted-by":"publisher","first-page":"6739","DOI":"10.1007\/s10489-021-02783-8","volume":"52","author":"Z Li","year":"2022","unstructured":"Li Z, Oskarsson M, Heyden A (2022) Detailed 3D human body reconstruction from multi-view images combining voxel super-resolution and learned implicit representation. Appl Intell 52:6739\u20136759","journal-title":"Appl Intell"},{"issue":"1","key":"1461_CR3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s11263-019-01217-w","volume":"128","author":"B Yang","year":"2020","unstructured":"Yang B, Wang S, Markham A et al (2020) Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction. Int J Comput Vision 128(1):53\u201373","journal-title":"Int J Comput Vision"},{"key":"1461_CR4","doi-asserted-by":"publisher","first-page":"2253","DOI":"10.1007\/s10489-020-02000-y","volume":"51","author":"PRS Devi","year":"2021","unstructured":"Devi PRS, Baskaran R (2021) SL2E-AFRE: Personalized 3D face reconstruction using autoencoder with simultaneous subspace learning and landmark estimation. Appl Intell 51:2253\u20132268","journal-title":"Appl Intell"},{"key":"1461_CR5","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.medengphy.2019.12.006","volume":"78","author":"S Migliori","year":"2020","unstructured":"Migliori S, Chiastra C, Bologna M et al (2020) Application of an OCT-based 3D reconstruction framework to the hemodynamic assessment of an ulcerated coronary artery plaque. Med Eng Phys 78:74\u201381","journal-title":"Med Eng Phys"},{"key":"1461_CR6","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.isprsjprs.2021.01.013","volume":"173","author":"JT Yang","year":"2021","unstructured":"Yang JT, Kang ZZ, Zeng LP et al (2021) Semantics-guided reconstruction of indoor navigation elements from 3D colorized points. ISPRS J Photogramm Remote Sens 173:238\u2013261","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1461_CR7","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.isprsjprs.2020.05.024","volume":"166","author":"Q Zhu","year":"2020","unstructured":"Zhu Q, Wang Z, Hu H et al (2020) Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction. ISPRS J Photogramm Remote Sens 166:26\u201340","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1461_CR8","doi-asserted-by":"crossref","unstructured":"Bitzidou M, Chrysostomou D, Gasteratos A (2012) Multi-camera 3D object reconstruction for industrial automation. In: IFIP Int Conference Adv Prod Manag Syst 526\u2013533","DOI":"10.1007\/978-3-642-40352-1_66"},{"issue":"2","key":"1461_CR9","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2014","unstructured":"Lowe DG (2014) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91\u2013110","journal-title":"Int J Comput Vision"},{"issue":"11","key":"1461_CR10","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.1109\/TPAMI.2014.2321376","volume":"36","author":"M Muja","year":"2014","unstructured":"Muja M, Lowe DG (2014) Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans Pattern Anal Mach Intell 36(11):2227\u20132240","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1461_CR11","doi-asserted-by":"crossref","unstructured":"Rublee E, Rabaud V, Konolige K et al (2012) ORB: An efficient alternative to SIFT or SURF. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2564\u20132571","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"1461_CR12","doi-asserted-by":"crossref","unstructured":"Bay H, Tuytelaars T, Gool LV (2006) SURF: speeded up robust features. In: Proceedings of European Conference on Computer Vision, pp 404\u2013417","DOI":"10.1007\/11744023_32"},{"key":"1461_CR13","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1137\/080732730","volume":"2","author":"JM Morel","year":"2009","unstructured":"Morel JM, Yu GS (2009) ASIFT: a new framework for fully affine invariant image comparison. SIAM J Imag Sci 2:438\u2013469","journal-title":"SIAM J Imag Sci"},{"key":"1461_CR14","doi-asserted-by":"crossref","unstructured":"Lin WYD, Cheng MM, Lu J et al (2014) Bilateral functions for global motion modeling. In: Proceedings of European Conference on Computer Vision, pp 341\u2013356","DOI":"10.1007\/978-3-319-10593-2_23"},{"issue":"8","key":"1461_CR15","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1016\/j.patcog.2015.02.026","volume":"48","author":"X Tan","year":"2015","unstructured":"Tan X, Sun C, Sirault X et al (2015) Feature matching in stereo images encouraging uniform spatial distribution. Pattern Recognit 48(8):2530\u20132542","journal-title":"Pattern Recognit"},{"issue":"9","key":"1461_CR16","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.imavis.2014.05.002","volume":"32","author":"F Bellavia","year":"2014","unstructured":"Bellavia F, Tegolo D, Valenti C (2014) Keypoint descriptor matching with context-based orientation estimation. Image Vision Comput 32(9):559\u2013567","journal-title":"Image Vision Comput"},{"key":"1461_CR17","doi-asserted-by":"crossref","unstructured":"Lin WY, Cheng MM, Shuai Z et al (2013) Robust non-parametric data fitting for correspondence modeling. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2376\u20132383","DOI":"10.1109\/ICCV.2013.295"},{"issue":"9","key":"1461_CR18","doi-asserted-by":"publisher","first-page":"2321","DOI":"10.1109\/TPAMI.2019.2939530","volume":"42","author":"A Scholefield","year":"2020","unstructured":"Scholefield A, Ghasemi A, Vetterli M (2020) Bound and Conquer: improving triangulation by enforcing consistency. IEEE Trans Pattern Anal Mach Intell 42(9):2321\u20132326","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"1461_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2602142","volume":"33","author":"Y Lipman","year":"2014","unstructured":"Lipman Y, Yagev S, Poranne R et al (2014) Feature matching with bounded distortion. ACM Trans Graph 33(3):1\u201314","journal-title":"ACM Trans Graph"},{"key":"1461_CR20","doi-asserted-by":"crossref","unstructured":"Maier J, Humenberger M, Murschitz M et al (2016) Guided matching based on statistical optical flow for fast and robust correspondence analysis. In: Proceedings of European Conference on Computer Vision, pp 101\u2013117","DOI":"10.1007\/978-3-319-46478-7_7"},{"issue":"7","key":"1461_CR21","doi-asserted-by":"publisher","first-page":"2110","DOI":"10.1109\/TIP.2015.2416639","volume":"24","author":"C Wang","year":"2015","unstructured":"Wang C, Wang L, Liu LQ (2015) Density maximization for improving graph matching with its applications. IEEE Trans Image Process 24(7):2110\u20132123","journal-title":"IEEE Trans Image Process"},{"key":"1461_CR22","doi-asserted-by":"crossref","unstructured":"Lin WY, Liu SY, Jiang NJ et al (2016) RepMatch: robust feature matching and pose for reconstructing modern cities. In: Proceedings of European Conference on Computer Vision, pp 562\u2013579","DOI":"10.1007\/978-3-319-46448-0_34"},{"key":"1461_CR23","doi-asserted-by":"crossref","unstructured":"Bian JW, Lin WY, Matsushita Y et al (2017) GMS: grid-based motion statistics for fast, ultra-robust feature correspondence. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2828\u20132837","DOI":"10.1109\/CVPR.2017.302"},{"issue":"6","key":"1461_CR24","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1007\/s11263-019-01280-3","volume":"128","author":"JW Bian","year":"2020","unstructured":"Bian JW, Lin WY, Liu Y et al (2020) GMS: grid-based motion statistics for fast, ultra-robust feature correspondence. Int J Comput Vision 128(6):1580\u20131593","journal-title":"Int J Comput Vision"},{"key":"1461_CR25","doi-asserted-by":"crossref","unstructured":"Lin WY, Wang F, Cheng MM et al (2018) CODE: coherence based decision boundaries for feature correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence 34\u201347","DOI":"10.1109\/TPAMI.2017.2652468"},{"issue":"9","key":"1461_CR26","doi-asserted-by":"publisher","first-page":"10576","DOI":"10.1007\/s10489-021-02990-3","volume":"52","author":"L Yang","year":"2022","unstructured":"Yang L, Huang Q, Li X et al (2022) Dynamic-scale grid structure with weighted-scoring strategy for fast feature matching. Appl Intell 52(9):10576\u201310590","journal-title":"Appl Intell"},{"key":"1461_CR27","doi-asserted-by":"crossref","unstructured":"Wang LB, Chen BB, Xu P et al (2020) Geometry consistency aware confidence evaluation for feature matching. Image Vision Comput 103:103984","DOI":"10.1016\/j.imavis.2020.103984"},{"issue":"3","key":"1461_CR28","first-page":"437","volume":"32","author":"YY Nie","year":"2020","unstructured":"Nie YY, Hu LH, Zhang JF et al (2020) Feature matching based on grid and multi-density for ancient architectural images. J Comput Aided Design Comput Graph 32(3):437\u2013444","journal-title":"J Comput Aided Design Comput Graph"},{"issue":"5","key":"1461_CR29","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1007\/s11263-018-1117-z","volume":"127","author":"JY Ma","year":"2019","unstructured":"Ma JY, Zhao J, Jiang JJ et al (2019) Locality preserving matching. Int J Comput Vision 127(5):512\u2013531","journal-title":"Int J Comput Vision"},{"issue":"6","key":"1461_CR30","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"M Fischler","year":"1987","unstructured":"Fischler M, Bolles R (1987) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381\u2013395","journal-title":"Commun ACM"},{"issue":"43","key":"1461_CR31","first-page":"1199","volume":"7","author":"HL Guo","year":"2020","unstructured":"Guo HL, Xia GB, Yan Y (2020) A preference-statistic-based data representation for robust geometric model fitting. Chinese J Comput 7(43):1199\u20131214","journal-title":"Chinese J Comput"},{"key":"1461_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3344294","author":"Z Xiao","year":"2023","unstructured":"Xiao Z, Tong H, Qu R et al (2023) CapMatch: semi-supervised contrastive transformer capsule with feature-based knowledge distillation for human activity recognition. IEEE Trans Neural Networks Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2023.3344294","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"issue":"1","key":"1461_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TETCI.2023.3304948","volume":"8","author":"Z Xiao","year":"2023","unstructured":"Xiao Z, Xing H, Zhao B et al (2023) Deep contrastive representation learning with self-distillation. IEEE Trans Emerg Topics Comput Intell 8(1):3\u201315","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"issue":"12","key":"1461_CR34","doi-asserted-by":"publisher","first-page":"14178","DOI":"10.1007\/s10489-022-03372-z","volume":"52","author":"B Lai","year":"2022","unstructured":"Lai B, Liu W, Wang C et al (2022) 2D3D-MVPNet: Learning cross-domain feature descriptors for 2D\u20133D matching based on multi-view projections of point clouds. Appl Intell 52(12):14178\u201314193","journal-title":"Appl Intell"},{"issue":"3\u20134","key":"1461_CR35","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1016\/j.mcm.2010.11.016","volume":"54","author":"YF Hu","year":"2011","unstructured":"Hu YF (2011) Research on a three-dimensional reconstruction method based on the feature matching algorithm of a scale-invariant feature transform. Math Comput Modell 54(3\u20134):919\u2013923","journal-title":"Math Comput Modell"},{"key":"1461_CR36","unstructured":"Stumpf A, Malet JP, Allemand P et al (2013) Robust affine-invariant feature points matching for 3D surface reconstruction of complex landslide scenes. In: EGU General Assembly, pp. EGU2013\u20136203"},{"key":"1461_CR37","doi-asserted-by":"crossref","unstructured":"Liu SM, Zhu WQ, Zhang CQ et al (2017) 3D reconstruction of indoor scenes using RGB-D monocular vision. Microcomput Appl 1\u20137","DOI":"10.1109\/ICRIS.2016.116"},{"issue":"9","key":"1461_CR38","doi-asserted-by":"publisher","first-page":"2246","DOI":"10.1109\/TMM.2019.2957984","volume":"22","author":"K Sun","year":"2020","unstructured":"Sun K, Tao W, Qian Y (2020) Guide to Match: multi-layer feature matching with a hybrid gaussian mixture model. IEEE Trans Multimed 22(9):2246\u20132261","journal-title":"IEEE Trans Multimed"},{"key":"1461_CR39","doi-asserted-by":"crossref","unstructured":"Strecha C, Hansen WV, Gool LV et al (2008) On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1\u20138","DOI":"10.1109\/CVPR.2008.4587706"},{"key":"1461_CR40","unstructured":"(2018) National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. Datasets for 3D reconstruction [Online], available: http:\/\/vision.ia.ac.cn\/data"},{"key":"1461_CR41","unstructured":"Wu CC (2011) VisualSfM: A visual structure from motion system. [Online], available: http:\/\/ccwu.me\/vsfm\/"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01461-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01461-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01461-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T17:25:31Z","timestamp":1721237131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01461-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,11]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1461"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01461-9","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2024,5,11]]},"assertion":[{"value":"4 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}